Nowadays, existing methods [1]}, [2]}, [3]} mainly deal with the imbalance problem by weighting small OARs in the loss function for training, which is a class-level weighting and ignores hard voxels in easy or large OARs. Second, due to the mechanism of CT imaging, soft tissues including OARs have a low contrast, such as the brain stem depicted in Fig. REF . The low contrast results in some uncertainty and challenges for accurate classification of voxels around the boundary of OARs. Most existing works [2]}, [3]}, [6]} do not deal with this problem well as they use a single intensity window/level for intensity transform, where a naive linear function between a lower threshold and an upper threshold is used. Such a simple intensity transform cannot provide good visibility of multiple organs at the same time, which limits the accuracy of multi-organ segmentation. Thirdly, HAN CT images are usually acquired with high in-plane resolution while low through-plane resolution, resulting in the smaller slice number of tiny OARs, such as the optical nerves highlighted by yellow arrows in Fig. REF . Therefore, using standard 3D convolutions to treat \(x\) , \(y\)  and \(z\)  dimensions equally [1]}, [2]}, [3]}, [6]} may overlook the anisotropic spatial resolution and limit the accuracy of segmentation of these small organs.
